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We're looking for a Senior Applied AI Scientist to sit at the frontier of AI security - turning emerging threats into the detection models that protect how AI is used inside the world's largest organizations. You'll be part of the AI Security Research department, working hand-in-hand with security researchers to translate threat intelligence into trainable signals that catch malicious behavior and security risks across the AI-powered workflows of Fortune 500 companies.

You'll bring deep technical versatility - reaching for classical ML, deep learning, or agentic based approaches based on what the problem demands, and the evaluation rigor to know when a model is truly ready for the real world. If you want to define what AI security engineering looks like, not just practice it, this role is for you.

What Youll Do
Responsibilities:
Build, train, and ship detection models end-to-end, from raw data to production.
Choose the right method for each problem - traditional ML, deep learning, fine-tuned LLMs, agents or heuristics - based on theoretical insights turned into practical results.
Partner with security researchers to turn security research outputs and domain expertise into detection capabilities.
Own evaluation: design benchmarks, build labeled datasets, and define production standards.
Monitor models in production across all paradigms - ML, deep learning, LLM-based, and agentic systems to track degradation and ensure reliability
Iterate fast, with a tight feedback loop between model performance and product outcomes.

This is a research‑first role focused on deeply understanding LLM internals to improve the security of AI agents. Youll design careful experiments on activations and interpretable features- e.g., probing, attribution & ablation/patching, representation‑geometry analyses-to uncover mechanisms behind jailbreak, indirect prompt injection, and other attacks. Then translate those insights into signals that can be used for detection and analysis of a model response.

The field of LLM interpretability at scale is exploding, with several major publications in the last months, and major opportunities for innovation.

What Youll Do
Responsibilities:
Investigate model internals, including activation/features analysis, unsupervised clustering, discovery of directions in latent space, etc. It may also require training specific model parts to improve interpretability metrics.
Design security‑grounded evaluations: curate datasets for different attack types, evaluate performance of different white box (model internals) methods compared to black box (input/output only) baselines.
Publish and share: produce our Labs posts and open artifacts; when the work is strong, aim for tier‑1 ML venues (NeurIPS, ICML, etc.) and security forums. A publication of code and/or trained models in cases of community relevant novelty.
Build tools: Several open source libraries exist (like Anthropics attribution graphs infra), but the research in the field is very dynamic, which will require you to build and adapt tools to your own research directions. This also includes agents to automate research work and distill knowledge from designed experiments.

As a Senior AI Engineer on the Guardian Agents team, you will develop both our autonomous "digital employees" that execute security programs and the Control Plane that governs them. You will build a platform that allows customers to calibrate agents through conversation, ensuring human feedback is transformed into persistent organizational knowledge. By engineering these closed-loop systems, you enable agents to learn, evolve, and deliver accurate, goal-driven security outcomes at scale.

What Youll Do
Responsibilities:
Architect Autonomous Agents: Build goal-driven agents for complex security tasks like false-positive reduction and automated triage.
Build the Control Plane: Develop the infrastructure for Human-in-the-Loop (HITL) feedback, natural language calibration, and stateful memory systems.
Engineer for Growth: Implement evaluation frameworks and "organizational memory" to ensure agents stay accurate and follow historical security precedents.
Bridge AI & Engineering: Collaborate in a "Core Pod" with Security Researchers and Data Scientists to turn raw security signals into scalable, productized agent behaviors.
Scale Data Pipelines: Integrate agents with our Big Data foundation to ensure real-time context and tenant-safe execution.

We're looking for a Principal Product Manager to define and drive our product strategy across our strategic technology partners. This role sits at the intersection of product management, technology strategy, and customer discovery. You'll work closely with product and engineering leaders at leading technology companies to identify opportunities, shape joint product experiences, and deliver integrations that create meaningful value for our customers. You'll own the product vision for our ecosystem-discovering new capabilities, influencing roadmaps, and ensuring we delivers a seamless experience across the platforms our customers use every day.

What Youll Do
Responsibilities
Define and execute our product strategy for strategic technology partnerships, prioritizing ecosystem investments based on customer needs, business impact, and strategic value.
Build trusted relationships with Product Managers, Architects, and Engineering teams at leading technology partners, and influence their roadmaps by collaborating closely on APIs, platform capabilities, and joint initiatives.
Identify customer problems that can be solved through joint product experiences, and define compelling "Better Together" solutions that drive customer value and differentiate Zenity.
Own the end-to-end product lifecycle for strategic integrations - from discovery and prioritization to launch and adoption - collaborating with Engineering, Design, Product Marketing, Sales, and Customer Success along the way.
Define success metrics and continuously measure adoption, customer impact, and business outcomes.
Represent Zenity in product discussions with strategic technology partners and customers.

The CTO Office is our core discovery engine, dedicated to navigating the rapid evolution in AI agent security and building the lighthouse. We are a high-agency team of ICs with integrated Engineering, Research, and Product mindsets. Our mission: incubate new ideas, define the approach to emerging attack classes, and build working POCs that prove technical reality. Guided by the CTO, Product, and GTM, we validate hard, open problems and actively support the Product organization by opening new development paths and seamlessly handing over advanced capabilities.

What Youll Do
Responsibilities:
Take a hard, open problem in AI agent security from this might be possible to a working POC that proves the company`s approach, grounded in technical reality.
Partner closely with Product, R&D, our Labs, and GTM. Dont just write about emerging problems. Think them through, design the solution, and POC it so customers see something real.
Own the end-to-end loop: scope the problem, build the prototype, evaluate it, document the approach, ship it.
Make the calls on architecture, tooling, and tradeoffs.
When a POC succeeds, own the handover to Engineering and Product. When a new direction needs its own team, lead the incubation.
Stay ahead of the rapid evolution in AI and proactively identify emerging trends.

We are looking for a Lead Software Engineer - Innovation Team to join our growing our new engineering innovation team who build our new product offering with our clients!
Engage is' newest product line - a small, fast-moving team operating like a startup inside the company. While ' core offering helps customers acquire better users and spend less doing it, Engage turns that intelligence inward: re-activating churned, dormant, and low-value users who are already on our customers' platforms. As a Senior Software Engineer, you'll work directly alongside the team's engineering lead, building the systems that take machine-learning predictions about user behavior and turn them into automated, continuously-learning decisions delivered straight into our customers' DB and CRM tools. This is a role with no narrow lane - you'll touch literally everything: data pipelines, ML and algorithms, applicative backend services, and even the UI. You'll partner closely with a VP Product and senior data scientists in a high-ownership environment where you shape the architecture, ship to production, and see your work drive a brand-new revenue stream for the company.

We're looking for a Field CTO / Principal Architect to serve as our most senior technical voice in the field - trusted by CTOs, CISOs, and engineering leaders at our most strategic enterprise accounts, and a technical backbone for our Pre-Sales and Field organization.

This is a hybrid role at the intersection of customer-facing technical leadership, security expertise, and field enablement. You'll lead high-stakes architecture conversations and proof-of-concept engagements, serve as an escalation point for both customer-facing technical challenges and internal Product/Engineering alignment, and build the playbooks and materials that make every Sales Engineer and Pre-Sales professional more effective.

What Youll Do
Responsibilities:
Serve as the primary senior technical presence in strategic and enterprise accounts, building trusted relationships with CTOs, CISOs, architects, and senior engineering leaders.
Lead architecture sessions, executive briefings, and POCs that demonstrate the depth and differentiation of the platform, while being the voice of technical truth in the sales process.
Help enterprise customers understand their AI agent risk surface and build a credible path toward governance, observability, and control.
Elevate the broader Sales Engineering team by sharing knowledge and building reusable technical assets (architecture guides, security frameworks, deployment playbooks).
Partner with GTM on positioning, technical objection handling, and competitive differentiation in senior-level enterprise deals, and represent us at industry events, advisory boards, and analyst briefings.
Bring structured feedback from the field to Product, Engineering, and Research, identifying patterns across accounts and influencing roadmap priorities based on what top customers need.